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shapes detection - contour approximation with OpenCV

Asked 2011-06-12T13:11:16.610
15

I'm writing small application for shape detections. What I need to do in a first place is to find the most significant shape on an image. I started from some preprocessing including converting image to grayscale, thresholding and edge detection. Image before and after these operations is presented below

Before

enter image description here

After

enter image description here

So as You can see the main shape is visible (however it is a bit scattered) and there are also some noises (small trees etc). What I need to do is to extract somehow only the most significant shape (the biggest one) - in this case it is a tower. What I wanted to do is use contour finding function in opencv and then somehow aproximate found conturs with polygon. Then I would (somehow) calculate area of countours and select only the biggest one. So far I manged (only) to find contours using 

cvFindContours(crated,g_storage,&contours);

I know that there is a 

cvApproxPoly

function , however I am not able to get any usefull information for the result of this function. Could somebody tell me if it is possible to calculate area of contour or to approximate the contur with polygon. Maybe You have a better idea how to extract only the most significant shape ?

c++ image-processing opencv
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5

Your main problem is that the tower contour is scattered. It will be hard to recreate whole contour from those little pieces. Optimize your edge detection phase (try cvAdaptiveThreshold), or use different approach (maybe something like object segmentation)

After you have your contour in one piece, you can check its area like this:

CvSeq* convex_hull=cvConvexHull2( contour, storage, CV_CLOCKWISE, 2 );
CvSeq* quad=cvApproxPoly(convex_hull, sizeof(CvContour), storage, CV_POLY_APPROX_DP, cvContourPerimeter(contour)*0.02, 0);
float size=fabs(cvContourArea( quad,CV_WHOLE_SEQ,0 ));

You will need to tune the parameters. It was used to detect rectangles.

answered 2011-06-12T20:52:25.497

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